Robotic Grasping for Instrument Manipulations

This project focuses on the grasp requirements derived from the voluntary and involuntary physical interactions in instrument manipulations. The manipulation-oriented grasp requirements include interactive wrench requirements and motion requirements that are required to accomplish a manipulation task. The manipulation-oriented grasp requirements are directly associated with the functionality of the instrument and the
manipulation task, but independent from the robotic hardware.
Grasp quality measures developed from the manipulationoriented grasp requirements can be used as search criteria for optimal grasps.

Facilitating force and torque in task

A grasp of the instrument in the interactive task should be
able to hold on to the instrument and provide the interactive wrench (force and torque) during the task. Therefore, how much the desired task wrench space is covered by the grasp wrench (capability of this grasp) should be used to evaluate how good the grasp is.

Facilitating motion in task

To perform a task, the instrument motion trajectory can either be computed by a motion planner or generated from a learned motion model. Grasping with a robotic hand give flexibility in “mounting” the instrument onto the robotic arm -- a different grasp will connect the instrument to the robotic arm with a different pose, then the inverse kinematics approach will result in a different joint motion to achieve the same functional
motion. Therefore, the grasp and the functional motion
decide the manipulator’s motion. With a desired functional tool
motion, the grasp determines the manipulator’s motion. Since
different manipulator motions will have different efficiency
rates in transferring the motion from joints to the instrument,
the efficiency of the manipulation motion should be used to
evaluate the grasp.

learning grasp strategies

The quality measures in both wrench coverage and manipulation
efficiency are determined by contact points of the robotic
hand on the tool, and contact points are further determined by
the hand posture as well as the relative hand position and
orientation. Therefore, a grasp G can be defined with an array
of finger joint angles and hand position and orientation. When
a robotic hand with high DOF is used, strategies learned from
demonstrations are introduced to reduce the search space.

Instead of learning grasping points, we extract and use two
more abstract strategies: grasp types and thumb placement.
They confine the configuration space, but leave enough room
for grasp planning to find the optimal stable grasp that is
adapted to different robotic hands.

A grasp type abstracts the manner in which a human grips
an object for a manipulation. It can either be input by a user
or recognized in a demonstration.
For robotic hands with less DOF, fewer grasp types can be
defined. Taking the Barrett hand as an example, we defined
only five grasp types, much fewer than the human hand: power
grasp, power sphere, precision grasp, precision sphere, and
lateral pinch. To map from the learned grasp types from human
grasps, some grasp types can be grouped into one.

For a particular instrument manipulation task, a grasp type
is learned from human demonstration using machine learning techniques and then mapped to the robotic hand’s grasp type.

Opposable?? thumb placement

There is general agreement in anthropology that thumb
plays a key role in gripping an object efficiently. The crucial
feature distinguishing human hand from other apes is the
opposable thumb. The same situation applies to robots that
almost all robotic hands have a long and strong opposable
thumb. Mapping only thumb position from a human hand to
a robot hand is simple because there is little correspondence
problem and it can be easily generalized to different robotic
hand models.

Both the grasp types and the thumb placement dramatically
reduce the hand configuration space for searching an optimal
grasp. For example, the constrains introduced by the thumb
placement of a Barrett Hand reduces its configuration dimension
from ten (six for the wrist and four for the fingers) to
two or three depending on grasp types.

Two different grasps would requires two very different arm motion to pour water

Patents

Y. Sun, Y. Lin, Systems and Methods for Planning a Robot Grasp Based upon a Demonstration Grasp, US patent ＃9,321,176 Issued on April 26, 2016.

This work is supported by NSF. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.